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Record W2901812131 · doi:10.38140/com.v15i0.963

Using visual data to 'save lives' in the age of AIDS?

2010· article· en· W2901812131 on OpenAlex
Thoko Mnisi, Naydene de Lange, Claudia Mitchell

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCommunitas · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsMcGill University
FundersNational Research Foundation
KeywordsStigma (botany)Citizen journalismContext (archaeology)Participatory action researchPublic relationsDigital literacyParticipatory designLiteracyWork (physics)Qualitative propertyQualitative researchSociologyPedagogyInternet privacyComputer sciencePsychologyPolitical scienceWorld Wide WebEngineeringGeographySocial science

Abstract

fetched live from OpenAlex

This article outlines the use of a digital archive, a data set of staged photos around HIV and Aids related stigma, with educators in two rural schools, exploring their views on using it in their teaching to address stigma. A qualitative research approach, using community-based participatory methodology, was used with educators in two rural schools. The findings suggest that the use of ICT in a rural context can enable educators to access, create and share digital material, which is relevant and realistic and individually tailored, in creative ways to address HIV and Aids related stigma in the school. Technology can facilitate community participation in the production of localknowledge, however, language, computer literacy and access continue to remain a barrier. This work is exploratory and encourages further work around how visual data in a digital archive can facilitate social change.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.890
GPT teacher head0.722
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it